Can CodeNinja 1.0 OpenChat 7B i1 run on NVIDIA T4 16GB?

YES — Runs Great

C50Usable

Estimated from fit model

CodeNinja 1.0 OpenChat 7B i1 needs ~7.9 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.9 GB, 48.7 tok/s, Runs well

7.9 GB required16.0 GB available

49% VRAM used

Fit status

Runs well

Decode

48.7 tok/s

TTFT

3974 ms

Safe context

174K

Memory

7.9 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on NVIDIA T4 16GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers))  # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
    """Sort a list using the quicksort algorithm.

    Args:
        arr: List of integers to sort.

    Returns:
        A new sorted list.
    """
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left   = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right  = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 48.7 tok/s decode · 4.0s TTFT (warm) · 122 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	48.7 tok/s	2168 ms	174K
Coding	C	Runs well	48.7 tok/s	3974 ms	174K
Agentic Coding	C	Runs well	48.7 tok/s	5781 ms	174K
Reasoning	C	Runs well	48.7 tok/s	4697 ms	174K
RAG	C	Runs well	48.7 tok/s	7226 ms	174K

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C46
Q3_K_S	3	3.4 GB	Low	C47
NVFP4	4	3.9 GB	Medium	C47
Q4_K_M	4	4.3 GB	Medium	C48
Q5_K_M	5	5.0 GB	High	C48
Q6_K	6	5.7 GB	High	C49
Q8_0Best for your GPU	8	7.5 GB	Very High	C51
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run CodeNinja 1.0 OpenChat 7B i1 on your machine.

Run

lms load hf-mradermacher--codeninja-1-0-openchat-7b-i1-gguf && lms server start

Opções de upgrade

Hardware que roda bem CodeNinja 1.0 OpenChat 7B i1

RX 7900 XT 20GBMelhor custo-benefício

20 GB VRAM (+4)800 GB/s (+480)

Raises estimated decode speed by about 101%.98 tok/s decodificação

Raises estimated decode speed by about 101%.

Adds memory headroom for longer context windows and future model growth.

~$899 MSRP

RTX A4500 20GBOpção econômica

20 GB VRAM (+4)640 GB/s (+320)

Raises estimated decode speed by about 101%.98 tok/s decodificação

Raises estimated decode speed by about 101%.

Adds memory headroom for longer context windows and future model growth.

~$2,000 MSRP

Frequently asked questions

Can NVIDIA T4 16GB run CodeNinja 1.0 OpenChat 7B i1?

Yes, NVIDIA T4 16GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Runs well). Expected decode speed: 48.7 tok/s.

How much VRAM does CodeNinja 1.0 OpenChat 7B i1 need?

CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 7.9 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeNinja 1.0 OpenChat 7B i1?

The recommended quantization for CodeNinja 1.0 OpenChat 7B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeNinja 1.0 OpenChat 7B i1 run at on NVIDIA T4 16GB?

On NVIDIA T4 16GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 48.7 tokens per second decode speed with a time-to-first-token of 3974ms using Q4_K_M quantization.

Can NVIDIA T4 16GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on NVIDIA T4 16GB receives a C grade with 48.7 tok/s and 174K context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on NVIDIA T4 16GB?

On NVIDIA T4 16GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 174K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA T4 16GB See all hardware for CodeNinja 1.0 OpenChat 7B i1

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